Summary Clean ERP data sitting in a data lake doesn’t answer the question every operations leader eventually asks: where exactly is our process breaking down? We built an automated pipeline that connects a client-facing web portal, Azure Table Storage, and Azure Databricks to a leading process mining platform, turning validated ERP data into a living view of how work actually flows. The pipeline is fully status-driven: every record is tracked from submission through processing to completion, with no manual exports or spreadsheet hand-offs. Purchase order data is modeled through a medallion architecture and delivered to the process mining platform, where AI-driven analysis automatically surfaces bottlenecks and deviations from the expected process. Business impact: process owners moved from static, after-the-fact reporting to a near real-time, evidence-based view of process performance. Table of Contents 01  About the Customer 05  The Six-Step Pipeline 02  The Challenge 06  Architecture Overview 03  The Solution 07  Business Impact 04  AI-Driven Process Mining 08  FAQs About the Customer Customer Spotlight A Leading Digital Transformation Partner — Europe Our customer is a leading enterprise headquartered in Europe, operating across diverse manufacturing and supply chain divisions. Having already standardized their ERP data through a medallion architecture on Databricks, leadership wanted to go a step further: not only manage ERP data at scale, but also connect it seamlessly into process mining tools to uncover how core processes truly perform in practice. The focus was on gaining operational clarity into workflows such as purchase order management, invoice handling, and procurement cycles. The Challenge Standardized, clean data answers “what happened.” It rarely answers “why is this taking so long” or “where exactly is this process breaking down.” The business kept running into the same limitations: 1Why do purchase orders take longer to close in some regions than others? 2Which approval step is quietly adding the most delay to the process? 3How do we get validated ERP data into a process analysis tool without manual exports every time? 4How do we know, at any point in time, what has been processed, what’s pending, and what failed? 5Can this insight be generated automatically, instead of requiring a manual investigation every quarter? The Solution We extended the existing Databricks-based data platform with an automated, status-driven delivery layer connecting a client web portal, Azure Table Storage, Azure Databricks, and a leading process mining platform, orchestrated end-to-end with minimal manual intervention. Status-Driven Orchestration Every record carries a live status, from initial submission through sync completion, tracked in Azure Table Storage. Automated Bulk Processing Azure Logic Apps trigger the pipeline through APIs, so batches of records are processed without manual intervention. Reusable Databricks Framework The same medallion pipeline used for data standardization models Purchase Order data for process mining. AI-Driven Process Analysis The process mining platform’s AI reconstructs the real, as-executed process and highlights bottlenecks automatically. The Six-Step Pipeline Here’s how a single record moves from submission to a fully synced, process-mining-ready state: ⚙ Client Web PortalEnd-to-end data pipeline · Azure + Databricks 6 steps 🌐 1) Website Input The user submits data via the client web portal, a form or API request initiates the pipeline. ↓ 🗃 2) Azure Table Sync Incoming data is written and synced into Azure Table Storage. ↓ 📁 3) Status Filter Records from Azure Table are filtered where status matches: ✓ Perfect🕑 Queue ↓ ⚡ 4) Databricks Pipeline The framework is executed through the Databricks pipeline, processing all filtered records in batch. ↓ 🔄 5) Azure Table Update Once the Databricks sync completes, status is updated in Azure Table: Queue→✓ Synced ↓ 📊 6) UI Reflection Synced data is reflected back to the client web portal UI for the end user. Architecture Overview Once records reach the “Synced” state, the same medallion architecture used for data standardization models Purchase Order Details and Purchase Order Lines and delivers them into the process mining platform: ERPExtracts Row-header files → Bronze Raw landing → Silver Cleansed & standardized → Gold Business-ready models → DeltaLake Parquet delivery → ProcessMining AI-driven analysis Because the framework is configuration-driven, the same architecture can extend to additional ERP data lake sources, SFTP feeds, or other cloud storage without a redesign. AI-Driven Process Mining Analysis With Purchase Order Details and Purchase Order Lines modeled and delivered on a reliable, automated cadence, the process mining platform’s AI reconstructs the real, as-executed purchase order process directly from the underlying event data. Instead of relying on assumptions about how the process should work, process owners see how it actually works: where orders stall, which approval paths deviate from the intended flow, and where cycle time is quietly being lost. “A purchase order may look fine on paper, but the process data tells you exactly where it got stuck, and that gap surfaces automatically.” Business Impact Before After Manual exports required to analyze process performance Fully automated, status-driven pipeline from intake to process mining No visibility into where a record stood in processing Live status tracking from submission through sync completion Process bottlenecks discovered through manual investigation AI-driven analysis surfaces deviations and delays automatically Static, after-the-fact process reporting Near real-time, evidence-based process visibility One-off integration effort per process area Reusable framework, extendable to other business processes Frequently Asked Questions Does this require a specific process mining platform? No. The pipeline delivers modeled, business-ready data through Delta Lake and Parquet, which can be connected to most modern process mining platforms. How often is data refreshed in the process mining platform? The pipeline is designed for batch processing on a defined schedule, and can be tuned toward near real-time delivery depending on business needs and source system constraints. Can this be extended beyond Purchase Order data? Yes. Because the framework is configuration-driven, the same approach can extend to other process areas such as order-to-cash or procure-to-pay. What happens if a record fails validation? Records that don’t meet the status criteria simply remain in a pending state and are not passed downstream, so failures are visible and traceable rather than silently dropped. Conclusion Clean data is the foundation, but process visibility is where the business … Continue reading From ERP Data to Process Mining Insights: Building an Automated Pipeline for Real-Time Process Visibility →

Summary Enterprises running large, multi-country ERP systems often extract data that is technically complete but practically unusable, split across duplicate language columns and encoded with undocumented numeric values. We built a self-service data platform on Azure so that business users, not just data engineers, could define, validate, and process ERP extracts without writing a single line of code. The solution resolves two of the most common ERP data problems: a single field like “Item Description” spread across nine language-specific columns, and reference fields like “Order Status” stored only as numeric codes. A custom web portal puts business users in control of table specifications, validation rules, and processing status, while Azure Databricks and Delta Lake quietly do the heavy lifting behind the scenes. Business impact: dozens of ERP tables moved from raw, multi-language, code-heavy extracts to a single, trusted, human-readable data layer, without adding headcount to the data engineering team. Table of Contents 01  About the Customer 05  Self-Service Data Onboarding 02  The Challenge 06  Medallion Architecture 03  The Solution 07  Business Impact 08  FAQs 09  Conclusion About the Customer Customer Spotlight A Leading Digital Transformation Partner — Europe Our customer is a leading enterprise headquartered in Europe, operating across diverse manufacturing and supply chain divisions. Having already standardized their ERP data through a medallion architecture on Databricks, leadership wanted to go a step further: not only manage ERP data at scale, but also connect it seamlessly into process mining tools to uncover how core processes truly perform in practice. The focus was on gaining operational clarity into workflows such as purchase order management, invoice handling, and procurement cycles. The Challenge Most organizations extracting data from a large ERP system successfully get the data out. The problem isn’t extraction, it’s making that data mean something the moment it lands. Business and IT teams found themselves asking the same questions on repeat: 1Why does the same field appear nine times, with a different value in each column? 2What does “Order Status= 3” actually mean, and who is the source of truth for that mapping? 3How much manual translation and lookup work happens before a single report can be trusted? 4Can business users resolve these issues themselves, without waiting weeks on an IT backlog? 5How do we scale this across dozens of tables without writing dozens of one-off scripts? Two problems came up again and again, and both are far more common across ERP implementations than most leadership teams realize. Multi-Language Columns Because the ERP system was configured for every Order Status the business operates in, a single logical field such as “Item Description” existed as up to nine separate columns, one per language: English, French, German, Spanish, and more. Reports built directly on top of the raw extract had no reliable way of knowing which column to use for which record. In practice, this meant a plant manager in France could open a report and see item names in German, while a sales report for the Spanish market silently pulled blank fields because the Spanish-language column hadn’t been populated for that record. The data was all there; it just wasn’t usable without someone manually deciding, table by table, which language column to trust. Undocumented Domain Values Reference fields like Country, Currency, and Order Status were stored as raw numeric codes rather than readable labels, for example Order Status: 1 = Completed , 2 = In Progress, 3 = Shipped. These mappings lived inside ERP configuration screens, not in the extracted data itself. That meant every downstream report, dashboard, or spreadsheet needed its own copy of the same lookup table, manually kept in sync. When a code changed or a new Order Status was added in the ERP, there was no guarantee every report using it would be updated at the same time, which meant leadership could be looking at the performance chart that was quietly wrong. The Solution Rather than writing custom transformation logic for every table (a solution that ages badly the moment a new table or region gets added), we designed a configuration-driven pipeline built on Azure Databricks, fronted by a self-service web application that puts control directly in the hands of business and functional users. Self-Service Web Portal Business users upload table specifications, review validation results, and queue tables for processing, entirely through a browser. Medallion Architecture Azure Databricks and Delta Lake refine raw extracts through Bronze, Silver, and Gold layers, without table-specific code. Automated Language Resolution Multi-language columns are detected and normalized automatically based on the specification, not hardcoded per table. Centralized Domain Mapping Numeric and coded reference values are resolved against a single, maintained lookup layer instead of scattered spreadsheets. Self-Service Data Onboarding: No Databricks Knowledge Required The centerpiece of the solution is a custom web application that lets a business or functional analyst, not a Databricks engineer, onboard a new ERP table from start to finish. Here’s what that looks like in practice: A business user uploads an Excel-based table specification defining the expected columns, data types, which fields are multi-language, and which fields are domain-coded and how to decode them. The portal validates the specification instantly, flagging missing mandatory columns or mismatches before any data is processed, so problems are caught at the source rather than three reports downstream. Once validation passes, the same user queues the table for processing with a single click. No notebook to open, no cluster to configure, no code to write or review. Behind the scenes, that specification feeds a generic, reusable Databricks framework that already knows how to apply the correct language resolution and domain-value decoding rules, so engineering effort doesn’t scale linearly with the number of tables. In effect, the portal turns “add a new ERP table to the analytics environment” from a data engineering request into a form a finance or operations analyst can complete in minutes, while still enforcing the same rigor and consistency a hand-built pipeline would require. Medallion Architecture on Databricks Once a table is queued through the portal, Azure Databricks takes over: Bronze: Raw ERP extracts are landed as-is, preserving … Continue reading How a Self-Service Data Portal Solved Multi-Language and Domain Value Chaos in ERP Data →

Stop Waiting on Reports — Databricks Genie | CloudFronts What You Will Learn Why dashboards alone are no longer enough for fast business decisions What Databricks Genie is and how it enables conversational access to your data How this changes the way finance, sales, and operations teams work What it means for your organization’s AI readiness and long-term decision-making Table of Contents 1. Let’s Start Here 2. The Challenge 3. The Solution — Databricks Genie 4. Business Impact 5. Frequently Asked Questions 6. Conclusion Let’s Start Here Organizations today are not short on data. They have dashboards, reports, and analytics tools in place. But when a business leader needs an answer to a specific question — one that no existing report covers — the usual path is to raise a request, wait for an analyst, and revisit it days later. That delay, small as it seems, adds up. Decisions get deferred. Opportunities get missed. And the data that was meant to drive the business ends up sitting behind a queue. Databricks Genie changes how organizations access their data — by making it conversational. The Challenge Dashboards were built to answer the questions someone thought of in the past. They are excellent for monitoring what is already defined — revenue trends, pipeline stages, operational metrics. But business does not move in straight lines. The moment a leader needs to investigate something outside of what was pre-built, the process breaks down: The question gets raised in a meeting — but no dashboard covers it It gets passed to a data analyst, who adds it to a queue behind other requests Days later, an answer arrives — often too late to influence the decision it was meant to support The result is a quiet, systemic gap between what the business senses and what the data can confirm in time. Leaders fill that gap with instinct. Risks go unspotted. Opportunities pass. Not because the data was not there — but because reaching it took too long. This pattern repeats across every function. Finance cannot investigate a cost anomaly until after month-end close. Sales leadership walks into a quarterly review with numbers someone else prepared. Operations learns about a supplier risk from a weekly report that arrives after the damage is done. The Solution — Databricks Genie Genie is the conversational AI interface built into Azure Databricks. It lets a business leader type a question in plain English — the same way they would ask a colleague — and get an answer drawn from the organization’s actual data, in seconds. There is no form to fill in. No report to request. No specialist to involve for every question. The leader asks, the data responds, and the conversation continues — narrowing, refining, following the next logical question — until the insight is clear enough to act on. The approach rests on three capabilities working together: Conversational access — questions in plain English return precise answers from live data, with no technical skill required from the business user Governed trust — Genie works within existing data permissions; every user sees only what they are authorized to access, and every answer shows the logic behind it Seamless fit — it connects to data the organization already holds, whether from ERP systems, CRM platforms, or operational sources, without requiring a new build This is not a replacement for dashboards. It is what happens between them — the investigative, in-the-moment layer that dashboards were never designed to provide. Business Impact The impact of conversational data access compounds across the organization over time: Decisions get made closer to the moment they matter — leaders investigate anomalies in real time, not after a two-day analysis cycle The right questions finally get asked — when the cost of asking drops to near zero, the volume and quality of insight-driven decisions goes up across every function Data teams focus on higher-value work — instead of fielding one-off requests, analysts build the data models and pipelines that generate lasting value Existing investments go further — Genie extends what the organization has already built, without requiring new infrastructure or a technology overhaul The organization becomes AI-ready — consistent, governed use of data at every level builds the foundation for more advanced AI capabilities to follow The organizations that embrace this shift early will not just be faster. They will be fundamentally better at acting on what they know — and that is an advantage that compounds over time. Frequently Asked Questions Do we need to replace our existing dashboards or BI tools? No. Genie works alongside what you already have. Dashboards remain the right tool for structured, recurring reporting. Genie handles the ad-hoc, investigative questions that dashboards were not built to answer. They complement each other. Does this require technical skills from business users? No. Genie is designed for business users who have no data or SQL background. Questions are asked in plain English — the same way you would ask a colleague — and answers are returned in a readable format without any technical input required. Is the data secure? Can users access data they should not see? Genie inherits the data permissions already configured in your organization’s data environment. Every user sees only what they are already authorized to access. There is no additional access granted by using Genie — governance is built in, not added on. Does our data need to be moved or rebuilt to use Genie? Not necessarily. If your organization’s data — from ERP, CRM, operational systems, or other sources — is already in the Databricks environment, Genie can work with it immediately. For organizations not yet on Databricks, CloudFronts can help assess the right path forward. How is this different from asking an AI chatbot a question about our business? A general AI chatbot answers from its training data — it does not know your organization’s numbers. Genie queries your actual data directly. Every answer is grounded in your real figures, with the source and logic visible, making … Continue reading Go Beyond Dashboards- How Databricks Genie Gives Every Business Leader Direct Access to Their Data →

Summary In modern data engineering environments, managing ingestion pipelines across multiple source systems becomes increasingly complex as data volume and variety grow. Hardcoded pipelines create maintenance overhead, slow down onboarding of new datasets, and introduce operational risks. This blog explains how a metadata-driven pipeline approach in Databricks can simplify ingestion by using a centralized configuration table to dynamically control pipeline behavior. It highlights how this pattern improves scalability, governance, and maintainability while enabling faster and more reliable data processing. The Real Problem: Hardcoded Pipelines Do Not Scale In many implementations, ingestion pipelines are built separately for each entity or source system. Typical issues include: As the number of entities grows, pipelines become difficult to manage and error-prone. What Is a Metadata-Driven Pipeline? A metadata-driven pipeline shifts control from code to configuration. Instead of writing separate logic for each dataset, we define ingestion behavior in a centralized configuration table. Typical metadata fields include: The pipeline reads this metadata and dynamically executes ingestion logic. Implementation Approach Step 1: Create a Configuration Table A centralized metadata table is created to define ingestion rules. Each row represents one dataset and contains all required configuration. Step 2: Dynamic Pipeline Execution The pipeline reads metadata and loops through each configuration entry. For each entity: No code changes are required when new entities are added. Step 3: Incremental Logic Control Instead of hardcoding: WHERE modifiedon > last_run The incremental field is read from metadata, allowing flexibility across different source systems. Step 4: Integration with Lakehouse Layers Metadata drives ingestion, while Lakehouse layers manage transformation. Why This Approach Works in Enterprise Environments 1. Scalability New entities can be added by inserting a new row in metadata. No pipeline duplication required. 2. Maintainability Changes in incremental logic or source structure are handled centrally. 3. Consistency All pipelines follow the same logic and standards. 4. Governance Metadata provides visibility into: Common Mistakes to Avoid Metadata-driven pipelines require discipline in design. Business Impact Metadata-driven pipelines are not just a technical optimization they are a foundational shift in how data platforms are built and managed. Organizations looking to scale their data engineering capabilities should move away from hardcoded ingestion logic and adopt configuration-driven approaches that support flexibility, governance, and long-term growth. Connect with CloudFronts to get started at transform@cloudfonts.com.

Summary Modern analytics platforms require structured data pipelines that ensure reliability, consistency, and governance across reporting systems. Traditional ETL approaches often struggle to scale as data volume and complexity increase. This blog explains how the Bronze–Silver–Gold (Medallion) architecture in Databricks provides a scalable and reliable framework for organizing data pipelines. It highlights how each layer serves a specific purpose, enabling better data quality, governance, and seamless integration with reporting tools such as Power BI. The Real Problem: Reporting Pipelines Become Fragile Over Time In many organizations: This leads to unreliable reporting and increased maintenance effort. What Is the Bronze–Silver–Gold Architecture? The Medallion architecture organizes data into three layers: Bronze Layer Raw data ingestion layer. Silver Layer Cleaned and standardized data. Gold Layer Business-ready, reporting-optimized data. Each layer has a clear responsibility. Bronze Layer: Raw Data Ingestion Purpose Key Characteristics Bronze acts as the system of record. Silver Layer: Data Standardization Purpose Key Activities Silver creates reusable datasets across reporting use cases. Gold Layer: Reporting-Ready Data Purpose Key Characteristics Gold tables are consumed directly by reporting tools. Why This Architecture Works 1. Separation of Concerns Each layer has a defined role, reducing complexity. 2. Improved Data Quality Data is progressively refined from raw to curated. 3. Better Performance Reporting queries run on optimized Gold tables. 4. Governance with Unity Catalog Access can be controlled at each layer: Common Implementation Mistakes These mistakes lead to long-term instability. Business Impact To conclude, the Bronze–Silver–Gold architecture provides a strong foundation for building scalable and reliable data pipelines in Databricks. When combined with proper governance and disciplined design, it enables organizations to deliver consistent, high-quality data for analytics and decision-making. We hope you found this article useful. If you would like to explore how AI-powered customer service can improve your support operations, please contact us at transform@cloudfronts.com.

The Challenge of Scaling AI in Enterprises Many organizations invest in AI initiatives but struggle to scale beyond pilot projects. Custom-built solutions are expensive, difficult to govern, and often limited to a single use case. As a result, AI investments fail to deliver sustained business value. Why Automation Alone Is Not Enough Traditional automation relies on rigid rules and predefined workflows. While effective for simple tasks, it cannot adapt to changing business conditions. Enterprises need intelligent systems that can reason, decide, and act autonomously. Understanding AI Agents in Simple Terms AI agents are intelligent software systems that understand goals, plan actions, and execute multi-step workflows with minimal human intervention. Unlike chatbots, AI agents do not just answer questions they act on insights. What Agent Bricks Bring to the Business Agent Bricks are modular, reusable AI agent components that accelerate enterprise AI adoption. They enable organizations to deploy intelligent agents quickly while maintaining security, governance, and compliance. Ask Me Anything: Execution Powered by Agent Bricks In the Ask Me Anything solution, Agent Bricks power the execution layer. They continuously evaluate enterprise data, identify project readiness gaps, and respond to leadership queries in real time. Agent Bricks Workflow Execution (Testing Screenshot) Use Case Spotlight: PMO Assistant at Scale The PMO Assistant built using Agent Bricks operates continuously, monitoring upcoming projects and flagging risks early. This reduces dependency on manual reporting and enables PMOs to focus on proactive delivery management. Business Value of an AI Workforce From a business perspective, Agent Bricks enable faster AI deployment, lower operational costs, and consistent decision-making across departments. Enterprises can scale AI solutions confidently without rebuilding logic for every new use case. Moving from Experiments to Execution To conclude, Agent Bricks help organizations move from isolated AI experiments to production-ready AI solutions. CloudFronts partners with enterprises to build scalable, governed AI workforces that deliver measurable business outcomes. I hope you found this blog useful, and if you would like to discuss anything or explore a future implementation, you can reach out to us at transform@cloudfonts.com.

In production Databricks environments, data issues such as accidental overwrites, faulty MERGE conditions, or incorrect backfills are common. Delta Lake’s Time Travel is not just a feature – it is a critical recovery and governance mechanism. This blog focuses only on practical recovery strategies that are actually used in real-world production systems. Why Time Travel Is Critical in Production Common failure scenarios include: •a. INSERT OVERWRITE wiping historical data • b. Incorrect MERGE conditions deleting valid records • c. Wrong filters during backfill corrupting data Reprocessing data is expensive and risky. Time Travel enables instant rollback with minimal impact. Version vs Timestamp (What You Should Use) Always prefer version-based time travel for recovery operations. Why version-based recovery is preferred: • a. Precise and deterministic • b. No time zone dependency • c. Safest option for production recovery Use timestamp-based queries only for auditing, not recovery. Identify the Last Safe State Before performing any recovery, always inspect the table history. DESCRIBE HISTORY crm_opportunities; Key fields to review: • a. version • b. timestamp • c. operation • d. userName This history acts as the single source of truth during incidents. Recovery Patterns That Actually Work 1. Partial Data Recovery (Recommended) Recover only the affected records instead of rolling back the entire table. Advantages: • a. No downtime • b. Safe for downstream reports • c. Most production-friendly approach 2. Full Table Restore (Use Carefully) Advantages: •a. Fast and atomic Risks: •a. Impacts all downstream consumers Use this approach only when the entire table is corrupted. Safe Validation Using CLONE Before restoring data in production, validate changes using a clone. Typical use cases: • a. Validate recovered data • b. Compare versions •c. Run business checks Retention & VACUUM (Most Common Mistake) The following command causes permanent data loss: Once vacuumed aggressively, time travel breaks and rollback becomes impossible. Production-Safe Retention Recommended retention: • a. Critical tables: 30 days • b. Reporting tables: 7–14 days Auditing & Root Cause Analysis (RCA) Track who changed data and when: Compare changes between versions: Key Best Practices • a. Capture table version before running risky jobs • b. Always use version-based time travel for recovery • c. Prefer partial recovery over full restores • d. Avoid aggressive VACUUM operations • e. Extend retention for critical tables • f. Validate using CLONE before restoring To conclude, Delta Lake Time Travel is not a backup mechanism, but it is the fastest and safest recovery tool in Databricks. When used correctly, it prevents downtime, reduces reprocessing cost, and improves production reliability. For enterprise Databricks pipelines, mastering this capability is mandatory, not optional. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

A Databricks Cluster is a group of virtual machines (VMs) in the cloud that work together to process data using Apache Spark.It provides the memory, CPU, and compute power required to run your code efficiently. Clusters are used for: Each cluster has two main parts: Types of Clusters Databricks supports multiple cluster types, depending on how you want to work. Cluster Type Use Case Interactive (All-Purpose) Clusters Used for notebooks, ad-hoc queries, and development. Multiple users can attach their notebooks. Job Clusters Created automatically for scheduled jobs or production pipelines. Deleted after job completion. Single Node Clusters Used for small data exploration or lightweight development. No executors, only one driver node. How Databricks Clusters WorkWhen you execute a notebook cell, Databricks sends your code to the cluster.The cluster’s driver node divides your task into smaller jobs and distributes them to the executors.The executors process the data in parallel and send the results back to the driver.This distributed processing is what makes Databricks fast and scalable for handling massive datasets. Step-by-Step: Creating Your First Cluster Let’s create a cluster in your Databricks workspace. Step 1: Navigate to Compute In the Databricks sidebar, click Compute. You’ll see a list of existing clusters or an option to create a new one. Step 2: Create a New Cluster Click Create Compute in the top-right corner. Step 3: Configure Basic Settings Step 4: Select Node Type Choose the VM type based on your workload. For development, Standard_DS3_v2 or Standard_D4ds_v5 are cost-effective. Step 5: Auto-Termination Set the cluster to terminate after 10 or 20 minutes of inactivity. This prevents unnecessary cost when the cluster is idle. Step 6: Review and Create Click Create Compute. After a few minutes, your cluster will turn green, indicating it is ready to run code. Clusters in Unity Catalog-Enabled Workspaces If Unity Catalog is enabled in your workspace, there are a few additional configurations to note. Feature Standard Workspace Unity Catalog Workspace Access Mode Default is Single User. Must choose Shared, Single User, or No Isolation Shared. Data Access Managed by workspace permissions. Controlled through Catalog, Schema, and Table permissions. Data Hierarchy Database → Table Catalog → Schema → Table Example Query SELECT * FROM sales.customers; SELECT * FROM main.sales.customers; When you create a cluster with Unity Catalog, you will see a new Access Mode field in the configuration page. Choose “Shared” if multiple users need to access governed data under Unity Catalog. Managing Cluster Performance and CostClusters can become expensive if not managed properly. Follow these tips to optimize performance and cost: a. Use Auto-Termination to shut down idle clusters automatically.b. Choose the right VM size for your workload. Avoid oversizing.c. Use Job Clusters for production pipelines since they start and stop automatically.d. Leverage Autoscaling so Databricks can adjust the number of workers dynamically.e. Monitor with Ganglia metrics to identify performance bottlenecks. Common Cluster Issues and Fixes Issue Cause Fix Cluster stuck starting VM quota exceeded or region issue Change VM size or region. Slow performance Too few workers or data skew Increase worker count or repartition data. Access denied to data Missing storage credentials Use Databricks Secrets or Unity Catalog permissions. High cost Idle clusters running Enable auto-termination. Best Practices for Using Databricks Clusters1. Always attach your notebook to the correct cluster before running it.2. Use development, staging, and production clusters separately.3. Keep the cluster runtime version consistent across environments.4. Terminate unused clusters to reduce cost.5. If you use Unity Catalog, prefer Shared clusters for collaboration. To conclude, clusters are the heart of Databricks.They provide the compute power needed to process large-scale data efficiently. Without them, Databricks Notebooks and Jobs cannot run. Once you understand how clusters work, you will find it easier to manage costs, optimize performance, and build reliable data pipelines. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

As data platforms mature, teams often face a familiar question:Should we continue with classic ETL pipelines, or move to Delta Live Tables (DLT)? Both approaches work. Both are widely used. The real challenge is knowing which one fits your use case, not which one is newer or more popular. In this blog, I’ll break down Delta Live Tables vs classic ETL from a practical, project-driven perspective, focusing on how decisions are actually made in real data engineering work. Classic ETL in Databricks Classic ETL in Databricks refers to pipelines where engineers explicitly control each stage of data movement and transformation. The pipeline logic is written imperatively, meaning the engineer decides how data is read, processed, validated, and written. Architecturally, classic ETL pipelines usually follow the Medallion pattern: Each step is executed explicitly, often as independent jobs or notebooks. Dependency management, error handling, retries, and data quality checks are all implemented manually or through external orchestration tools. This approach gives teams maximum freedom. Complex ingestion logic, conditional transformations, API integrations, and custom performance tuning are easier to implement because nothing is abstracted away. However, this flexibility also means consistency and governance depend heavily on engineering discipline. We implemented a Classic ETL pipeline in our internal Unity Catalog project, migrating 30+ Power BI reports from Dataverse into Unity Catalog to enable AI/BI capabilities. This architecture allows data to be consumed in two ways – through an agentic AI interface for ad-hoc querying and through Power BI for governed, enterprise-grade visualizations. We chose the ETL approach because it provides strong data quality control, schema stability, and predictable performance at scale. It also allows us to apply centralized transformations, enforce governance standards, optimize storage formats, and ensure consistent semantic models across reporting and AI workloads -making it ideal for production-grade analytics and enterprise adoption. Delta Live Tables Delta Live Tables is a managed, declarative pipeline framework provided by Databricks. Instead of focusing on execution steps, DLT encourages engineers to define what tables should exist and what rules the data must satisfy. From an architectural perspective, DLT formalizes the Medallion pattern. Pipelines are defined as a graph of dependent tables rather than a sequence of jobs. Databricks automatically understands lineage, manages execution order, applies data quality rules, and provides built-in monitoring. DLT pipelines are particularly well-suited for transformation and curation layers, where data is shared across teams and downstream consumers expect consistent, validated datasets. The platform takes responsibility for orchestration, observability, and failure handling, reducing operational overhead. In my next blog, I will demonstrate how to implement Delta Live Tables (DLT) in a hands-on, technical way to help you clearly understand how it works in real-world scenarios. We will walk through the creation of pipelines, data ingestion, transformation logic, data quality expectations, and automated orchestration. The Core Architectural Difference The fundamental difference between classic ETL and Delta Live Tables is how responsibility is divided between the engineer and the platform. In classic ETL, the engineer owns the full lifecycle of the pipeline. This provides flexibility but increases maintenance cost and risk. In Delta Live Tables, responsibility is shared: the engineer defines structure and intent, while Databricks enforces execution, dependencies, and quality. This shift changes how pipelines are designed. Classic ETL is optimized for control and customization. Delta Live Tables is optimized for consistency, governance, and scalability. When Classic ETL Makes More Sense Classic ETL is a strong choice when pipelines require complex logic, conditional execution, or tight control over performance. It is well suited for ingestion layers, API-based data sources, and scenarios where transformations are highly customized or experimental. Teams with strong engineering maturity may also prefer classic ETL for its transparency and flexibility, especially when governance requirements are lighter. When Delta Live Tables Is the Better Fit Delta Live Tables excels when pipelines are repeatable, standardized, and shared across multiple consumers. It is particularly effective for silver and gold layers where data quality, lineage, and operational simplicity matter more than low-level control. DLT is a good architectural choice for enterprise analytics platforms, certified datasets, and environments where multiple teams rely on consistent data definitions. A Practical Architectural Pattern In real-world platforms, the most effective design is often hybrid. Classic ETL is used for ingestion and complex preprocessing, while Delta Live Tables is applied to transformation and curation layers. This approach preserves flexibility where it is needed and enforces governance where it adds the most value. To conclude, Delta Live Tables is not a replacement for classic ETL. It is an architectural evolution that addresses governance, data quality, and operational complexity. The right question is not which tool to use, but where to use each. Mature Databricks platforms succeed by combining both approaches thoughtfully, rather than forcing a single pattern everywhere. Choosing wisely here will save significant rework as your data platform grows. Need help deciding which approach fits your use case? Reach out to us at transform@cloudfronts.com

As organizations scale their analytics platforms, governance often becomes the hardest problem to solve. Data may be accurate, pipelines may run on time, and reports may look correct, but without proper governance, the platform becomes fragile. We see this pattern frequently in environments where Power BI reporting has grown around a mix of SQL Server databases, direct Dataverse connections, shared storage accounts, and manually managed permissions. Over time, access control becomes inconsistent, ownership is unclear, and even small changes introduce risk. Unity Catalog addresses this problem by introducing a centralized, consistent governance layer across Databricks and downstream analytics tools like Power BI. The Governance Problem Most Teams Face In many data platforms, governance evolves as an afterthought. Access is granted at different layers depending on urgency rather than design. Common symptoms include: As reporting expands across departments like Finance, HR, PMO, and Operations, this fragmented governance model becomes difficult to control and audit. Why Unity Catalog Changes the Governance Model Unity Catalog introduces a unified governance layer that sits above storage and compute. Instead of managing permissions at the file or database level, governance is applied directly to data assets in a structured way. At its core, Unity Catalog provides: This shifts governance from an operational task to an architectural capability. A Structured Data Hierarchy That Scales Unity Catalog organizes data into a simple, predictable hierarchy: Catalog → Schema → Table This structure brings clarity to large analytics environments. Business domains can be separated cleanly, such as CRM, Finance, HR, or Projects, while still being governed centrally. For Power BI teams, this means datasets are easier to discover, understand, and trust. There is no ambiguity about where data lives or who owns it. Centralized Access Control Without Storage Exposure One of the biggest advantages of Unity Catalog is that access is granted at the data object level, not the storage level. Instead of giving Power BI users or service principals direct access to storage accounts, permissions are granted on catalogs, schemas, or tables. This significantly reduces security risk and simplifies access management. From a governance perspective, this enables: Power BI connects only to governed datasets, not raw storage paths. Cleaner Integration with Power BI When Power BI connects to Delta tables governed by Unity Catalog, the reporting layer becomes simpler and more secure. Benefits include: This model works especially well when combined with curated Gold-layer tables designed specifically for reporting. Governance at Scale, Not Just Control Unity Catalog is not only about restricting access. It is about enabling teams to scale responsibly. By defining ownership, standardizing naming, and centralizing permissions, teams can onboard new data sources and reports without reworking governance rules each time. This is particularly valuable in environments where multiple teams build and consume analytics simultaneously. Why This Matters for Decision Makers For leaders responsible for data, analytics, or security, Unity Catalog offers a way to balance speed and control. It allows teams to move quickly without sacrificing governance. Reporting platforms become easier to manage, easier to audit, and easier to extend as the organization grows. More importantly, it reduces long-term operational risk by replacing ad-hoc permission models with a consistent governance framework. To conclude, strong governance is not about slowing teams down. It is about creating a structure that allows analytics platforms to grow safely and sustainably. Unity Catalog provides that structure for Databricks and Power BI environments. By centralizing access control, standardizing data organization, and removing the need for direct storage exposure, it enables a cleaner, more secure analytics foundation. For organizations modernizing their reporting platforms or planning large-scale analytics initiatives, Unity Catalog is not optional. It is foundational. If your Power BI and Databricks environment is becoming difficult to govern as it scales, it may be time to rethink how access, ownership, and data structure are managed. We have implemented Unity Catalog–based governance in real enterprise environments and have seen the impact it can make. If you are exploring similar initiatives or evaluating how to strengthen governance across your analytics platform, we are always open to sharing insights from real-world implementations. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com